Optimization on the orthogonal group for independent component analysis

This paper derives a new algorithm that performs independent component analysis (ICA) by optimizing the contrast function of the RADICAL algorithm. The core idea of the proposed optimization method is to combine the global search of a good initial condition with a gradient-descent algorithm. This new ICA algorithm performs faster than the RADICAL algorithm (based on Jacobi rotations) while still preserving, and even enhancing, the strong robustness properties that result from its contrast. © Springer-Verlag Berlin Heidelberg 2007.

Lyapunov functions for stable cascades and applications to global stabilization

This paper generalizes recent Lyapunov constructions for a cascade of two nonlinear systems, one of which is stable rather than asymptotically stable. A new cross-term construction in the Lyapunov function allows us to replace earlier growth conditions by a necessary boundedness condition. This method is instrumental in the global stabilization of feedforward systems, and new stabilization results are derived from the generalized construction.

Orthogonal multipulse modulation for 64 Gigabit Fibre Channel

Real-time orthogonal multipulse modulation is demonstrated at 56 Gb/s with transmission over 500 m of single-mode fiber. Up to 2 dBo power budget advantage is predicted relative to alternatives such as PAM4. © 2013 OSA.

Testing of the multi-objective alliance algorithm on benchmark functions

A new version of the Multi-objective Alliance Algorithm (MOAA) is described. The MOAA's performance is compared with that of NSGA-II using the epsilon and hypervolume indicators to evaluate the results. The benchmark functions chosen for the comparison are from the ZDT and DTLZ families and the main classical multi-objective (MO) problems. The results show that the new MOAA version is able to outperform NSGA-II on almost all the problems.

Orthogonal multipulse modulation in optical datacommunications

Optical datacommunication links are now required at rates beyond 40 Gb/s. Achieving robust low-cost transmission at these rates necessitates consideration of advanced modulation formats. This paper concentrates on schemes using multiple orthogonal pulses and outlines the advantages relative to alternatives such as multilevel modulation. Recent real-time experimental results at 56 Gb/s are described. © 2013 IEEE.

Orthogonal multipulse modulation for 64 gigabit fibre channel

Real-time orthogonal multipulse modulation is demonstrated at 56 Gb/s with transmission over 500 m of single-mode fiber. Up to 2 dBo power budget advantage is predicted relative to alternatives such as PAM4. © 2013 OSA.

Orthogonal multipulse modulation for next-generation datacommunication links

Orthogonal multipulse modulation is demonstrated to allow ≈30 Gb/s real-time transmission over multimode fibre using an 850 nm VCSEL. The scheme eases considerably component bandwidth requirements compared with conventional NRZ modulation. © 2011 OSA.

Coarse optical orthogonal frequency division multiplexing for optical datacommunication applications

We propose a new low-cost solution using orthogonal transmission of non-return-tozero and carrierless-amplitude-and-phase format data to realize a coarse OFDM transmission system. Using low bandwidth electronics and optoelectronic components, the system is demonstrated at 37.5Gb/s. © OSA/ CLEO 2011.

100 Gb/s uncooled DWDM using orthogonal coding for low-cost datacommunication links

We demonstrate a 10 x 10 Gb/s uncooled DWDM system using orthogonal coding on adjacent carriers, assuming the use of a monolithically integrated sources. A power saving of 72% is expected over traditional WDM. © 2014 OSA.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.